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Regression Analysis - Mastering the Art of Regression Analysis Predict Analyze Decide - cover

Regression Analysis - Mastering the Art of Regression Analysis Predict Analyze Decide

Fouad Sabry

Publisher: One Billion Knowledgeable

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Summary

What is Regression Analysis
 
In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable and one or more independent variables. The most common form of regression analysis is linear regression, in which one finds the line that most closely fits the data according to a specific mathematical criterion. For example, the method of ordinary least squares computes the unique line that minimizes the sum of squared differences between the true data and that line. For specific mathematical reasons, this allows the researcher to estimate the conditional expectation of the dependent variable when the independent variables take on a given set of values. Less common forms of regression use slightly different procedures to estimate alternative location parameters or estimate the conditional expectation across a broader collection of non-linear models.
 
How you will benefit
 
(I) Insights, and validations about the following topics:
 
Chapter 1: Regression analysis
 
Chapter 2: Least squares
 
Chapter 3: Gauss-Markov theorem
 
Chapter 4: Nonlinear regression
 
Chapter 5: Coefficient of determination
 
Chapter 6: Instrumental variables estimation
 
Chapter 7: Omitted-variable bias
 
Chapter 8: Ordinary least squares
 
Chapter 9: Residual sum of squares
 
Chapter 10: Simple linear regression
 
Chapter 11: Generalized least squares
 
Chapter 12: Heteroskedasticity-consistent standard errors
 
Chapter 13: Variance inflation factor
 
Chapter 14: Non-linear least squares
 
Chapter 15: Principal component regression
 
Chapter 16: Lack-of-fit sum of squares
 
Chapter 17: Leverage (statistics)
 
Chapter 18: Polynomial regression
 
Chapter 19: Errors-in-variables models
 
Chapter 20: Linear least squares
 
Chapter 21: Linear regression
 
(II) Answering the public top questions about regression analysis.
 
(III) Real world examples for the usage of regression analysis in many fields.
 
Who this book is for
 
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Regression Analysis.
Available since: 02/04/2024.
Print length: 378 pages.

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